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Prepare, format, and validate datasets for supervised fine-tuning and preference training.
Passed 23 of 26 test cases with the skill, and 15 of 26 without it.
and 8 more in the full report
8 fixed15 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
List all six mandatory metadata fields that must be present in a dataset card for Phase 2 validation.
| Checked | Without skill | With skill |
|---|---|---|
| requires provenance details traceable to training data sources | Passed | Passed |
| requires total row counts and per-split breakdown | Missed | Passed |
The instructions your model reads when the skill loads.
This skill assumes finetuning-method-selection
already routed here — the next step is preparing
data, not choosing a method. What follows: format
selection by target method, the template/packing
mechanics behind the most common silent training
failures, rules for mixing in synthetic data
without collapse, and the dataset card that closes
out Phase 2 before a run starts.
Input: raw examples (demonstrations, preference
judgments, or task prompts) plus a routing decision
from finetuning-method-selection.
Output format: a formatted, packed, validated
JSONL dataset plus a completed dataset card — the
Phase 2 artifact /finetune checks before launching
training.
| Method | Shape | Rows |
|---|---|---|
| SFT, single-turn | Instruct (instruction/response or prompt/completion) | ~1,000+ floor |
| SFT, multi-turn | Conversation / ChatML messages list | ~1,000+ floor |
| DPO / ORPO | Preference pair (prompt, chosen, rejected) | Method-dependent, see preference-optimization |
| KTO | Unpaired (prompt, completion, label) | Method-dependent, see preference-optimization |
| GRPO / RLVR | Prompt-only (prompt + verifier metadata) | Method-dependent, see grpo-rlvr-training |
~1,000+ rows is the recommended floor for SFT, not a target. Below it, a handful of low-quality or duplicate examples can dominate the gradient; above it, quality over quantity — a smaller verified, deduplicated set beats a larger noisy one.
The ChatML shape, for orientation; the other four
formats plus a ShareGPT conversion note live in
references/formats-and-templates.md:
{"messages": [
test cases passed, vs 15 / 26 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.